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Security and Privacy Challenges in SDN-Enabled IoT Systems: Causes, Proposed Solutions,and Future Directions

computers, Materials & Continua 2024年第8期80卷 2511-2533页

作者： Ahmad Rahdari Ahmad Jalili Mehdi Esnaashari Mehdi Gheisari Alisa A.Vorobeva Zhaoxi Fang Panjun Sun Viktoriia M.Korzhuk Ilya Popov Zongda Wu Hamid Tahaei Institute of Artificial Intelligence Shaoxing UniversityShaoxing312000China Department of Computer Engineering Gonbad Kavous UniversityGonbad-e Kavus49717-99151Iran Faculty of Computer Engineering K.N.Toosi University of TechnologyTehran196976-4499Iran Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Secure Information Technologies Department National Research University ITMOSt.Petersburg197101Russia School of Electrical and Computer Engineering Shiraz UniversityShiraz71946–84334Iran Department of Computer Science and Engineering Islamic Azad UniversityDamavand***Iran Department of R&D Shenzhen BKD Co.Ltd.Shenzhen518000China

Software-Defined Networking(SDN)represents a significant paradigm shift in network architecture,separating network logic from the underlying forwarding devices to enhance flexibility and centralize ***-rently,the Internet of Things(IoT)connects numerous devices to the Internet,enabling autonomous interactions with minimal human ***,implementing and managing an SDN-IoT system is inherently complex,particularly for those with limited resources,as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN *** findings of this study underscore the primary security and privacy challenges across application,control,and data planes.A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to establish sufficient secrecy and privacy *** investigations have explored cutting-edge methods,such as leveraging blockchain for transaction recording to enhance security and privacy,along with applying machine learning and deep learning approaches to identify and mitigate the impacts of Denial of Service(DoS)and Distributed DoS(DDoS)***,the analysis indicates that encryption and hashing techniques are prevalent in the data plane,whereas access control and certificate authorization are prominently considered in the control plane,and authentication is commonly employed within the application ***,this paper outlines future directions,offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based IoT ecosystem.

关键词： Security privacy-preserving software-defined network internet of things

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RIS-Assisted Integrated Sensing and Communication Systems: Joint Reflection and Beamforming Design

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 908-927页

作者： Ismail, Mohamed I. Shaheen, Abdullah M. Fouda, Mostafa M. Alwakeel, Ahmed S. Modern Academy for Engineering and Technology Department of Electronic Engineering and Communication Technology Cairo4410242 Egypt Suez University Faculty of Engineering Department of Electrical Engineering Suez8151650 Egypt Idaho State University College of Science and Engineering Department of Electrical and Computer Engineering PocatelloID83209 United States

The Integrated Sensing and Communication (ISAC) system merged with Reconfigurable Intelligent Surface (RIS) has recently received much attention. This paper proposes an intelligent metaheuristic version of Enhanced Artificial Ecosystem Optimizer (EAEO) for a suggested beamforming optimization framework in ISAC systems with RIS. Two RIS are utilized in the presented model to enhance the received signal-to-noise ratio (SNR) for multiple-input multiple-output (MIMO) communication systems. Also, each element of each RIS scatters the incoming signal with a controllable phase-shift, without increasing its power. The signal is transmitted by a Dual-Function Base Station (DFBS) that is integrated with the RIS, which performs both communication and sensing functions simultaneously. The ISAC system is designed to optimize the signal transmission through the RIS by maximizing the SNR to increase the overall performance. In the proposed EAEO version, a Fitness-Distance-Balance Model (FDBM) is combined with the standard Artificial Ecosystem Optimizer (AEO) to improve the quality of the solutions in multidimensional and nonlinear optimization scenarios. The simulation results show that the proposed EAEO algorithm improves the SNR of different users for different numbers of RIS elements. The SNR reaches 25 dB when using 200 RIS elements. Moreover, the proposed EAEO is tested on the IEEE Congress on Evolutionary Computation 2017 (IEEE CEC'17) test suite. A comparative analysis is conducted compared to the standard AEO and several recent algorithms. The proposed EAEO derives great effectiveness and robustness over the others as it provides a higher rate of success in achieving the global optimum point. © 2020 IEEE.

关键词： Signal to noise ratio

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Bluetooth Low Energy-Based Novel Power Efficient Buffalo Calving Detection Solution

IEEE Networking Letters

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IEEE Networking Letters 2025年第1期7卷 6-10页

作者： Raina, Radhika Jeet Singh, Kamal Kumar, Suman Jain, Sarthak Indian Institute of Technology Ropar Department of Electrical Engineering Rupnagar140001 India Guru Gobind Singh Indraprastha University Computer Science and Engineering New Delhi110078 India

This letter focuses on tackling the challenge of accurately determining the timing of buffalo calving while prioritizing power efficiency. To achieve this, a novel, compact, lightweight and power efficient device is designed for buffalo comfort and can be conveniently attached to the tail. The device wirelessly transmits data to a gateway using Bluetooth Low Energy (BLE). This functionality becomes particularly crucial when the tail movement increases in the last 12 hrs before calving and is regarded as a key behavioral indicator for predicting the onset of labor. Moreover, when an accelerometer is tied to the buffalo's tail, the Z axis, which represents the vertical axis perpendicular to ground is anticipated to show the most notable deflections during this period as discussed in the literature. Thus, to conserve power, data is only transmitted when significant tail movement is detected, typically 12 hrs before calving, i.e., when Mean Z >-3 m/s2. This approach reduces the device's power consumption, extending its battery life to more than 6.08 years (approx.) using 620 mAh / 3V battery. © 2019 IEEE.

关键词： Accelerometers

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ACDD: Automated COVID Detection using Deep Neural Networks

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IEIE Transactions on Smart Processing and Computing 2023年第6期12卷 518-525页

作者： Raza, Ghulam Musa Shoaib, Muhammad Kim, Byung-Seo Department of Software and Communications Engineering Hongik University Sejong Korea Republic of School of Electrical Engineering and Computer Science National University of Science and Technology Islamabad Pakistan

December 2019 witnessed the outbreak of a novel coronavirus, thought to have started in the Chinese city of Wuhan. The situation worsened owing to its quick spread across the globe, leading to a worldwide pandemic that became known as COVID-19. To suppress the pandemic, early detection of positive COVID-19 patients has become highly important. There is a lack of precise automated tool kits available for use in diagnosing medical conditions, so auxiliary diagnostic tools are in high demand. Important information about this virus can be extracted from X-ray images, which can be used in conjunction with advanced artificial intelligence. This study addresses the unavailability of physicians in remote areas, and the complex algorithm proposed in this study can find potential matches for patients in rural areas who need care. This could help to improve access to care for those who need it most. The purpose of this study is to develop a novel model that can automatically detect COVID-19 by utilizing chest X-ray images. The proposed model incorporates binary and multi-class classification and can be employed by radiologists for timely detection of the COVID-19 virus in an effective manner. © 2023 The Institute of Electronics and Information Engineers.

关键词： COVID-19

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An Empirical Study to Investigate the Impact of Sustainable Practices in Agile Software Development

An Empirical Study to Investigate the Impact of Sustainable ...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Sohail, Muhammad Taha Mughal, Shuja Abiha, Ume Abbas, Assad Ali, Mazhar Khan, Samee U. Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Department of Computer Science University of Victoria VictoriaBC Canada Department of Computer Science COMSATS University Abbottabad Campus Islamabad Pakistan Department of Electrical and Computer Engineering Mississippi State University United States

ISBN: (纸本)9798331510503

This research examines the integration of sustainability principles within Agile Software Development Life Cycle (SDLC) methodologies. While Agile frameworks such as GLUX emphasize user experience and adaptability, they often prioritize software stability primarily during design and implementation phases, potentially neglecting long-term sustainability considerations. Our study posits that incorporating sustainability as a core element in Agile SDLC is essential for developing software that demonstrates optimal performance, environmental consciousness, and long-term cost-effectiveness. Through a comprehensive systematic literature review, we analyzed 67 papers selected from an initial pool of 1900 studies, employing qualitative research methods to extract and evaluate hypotheses regarding sustainable practices in agile development. The findings reveal a significant gap in current Agile methodologies concerning long-term software viability. This deficiency impedes the software’s ability to adapt to evolving requirements, thus limiting its full potential. Our research proposes novel frameworks and approaches to address this limitation, aiming to ensure sustainability throughout the Agile development process. By identifying and analyzing relevant procedures and methods that contribute to sustainability within the Agile framework, this study provides valuable insights for both researchers and practitioners. The proposed integrative approach extends beyond immediate usability concerns, focusing on software products’ long-term adaptability and viability. This research contributes to the growing knowledge of sustainable software development and offers practical guidelines for implementing sustainability measures within Agile frameworks. The findings highlight the importance of improving software longevity and adaptability in a rapidly evolving technological landscape. © 2024 IEEE.

关键词： Sustainable development goals

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TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation 25

TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesio...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Iqbal, Shahzaib Zeeshan, Muhammad Mehmood, Mehwish Khan, Tariq Razzak, Imran Department of Electrical Engineering Islamabad Pakistan School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798350379037

Early detection of skin cancer relies on precise segmentation of dermoscopic images of skin lesions. However, this task is challenging due to the irregular shape of the lesion, the lack of sharp borders, and the presence of artefacts such as marker colours and hair follicles. Recent methods for melanoma segmentation are U-Nets and fully connected networks (FCNs). As the depth of these neural network models increases, they can face issues like the vanishing gradient problem and parameter redundancy, potentially leading to a decrease in the Jaccard index of the segmentation model. In this study, we introduced a novel network named TESL-Net for the segmentation of skin lesions. The proposed TESL-Net involves a hybrid network that combines the local features of a CNN encoder-decoder architecture with long-range and temporal dependencies using bi-convolutionallong-short-term memory (Bi-ConvLSTM) networks and a Swin transformer. This enables the model to account for the uncertainty of segmentation over time and capture contextual channel relationships in the data. We evaluated the efficacy of TESL-Net in three commonly used datasets (ISIC 2016, ISIC 2017, and ISIC 2018) for the segmentation of skin lesions. The proposed TESL-Net achieves state-of-the-art performance, as evidenced by a significantly elevated Jaccard index demonstrated by empirical results. © 2024 IEEE.

关键词： Convolutional neural networks

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Direct Edge-To-Edge Local-Learning-Assisted Model-Based Transfer Learning

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IEEE Internet of Things Journal 2024年第11期11卷 20223-20235页

作者： Huang, Zih-Syuan Lu, Ching-Hu Hwang, I-Shyan Hsinchu308 Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Yuan-Ze University Department of Computer Science and Engineering Taoyuan320 Taiwan

With the gradual advancement of technology in the production of consumer goods, the Internet of Things (IoT) systems have experienced rapid development, resulting in a massive amount of data that can be processed using deep neural networks. However, annotating the data and training the models require significant manpower, time, and computational resources. Transfer learning can address this problem. Traditional systems rely on centralized servers for transfer learning. Although several studies have proposed distributed systems for direct edge-To-edge (e2e) instance-based and feature-based transfer learning, they neglect model-based transfer learning (MTL) within the same domain. This leads to lower learning efficiency, lower privacy protection, and higher transmission costs. Therefore, our study proposes direct e2e local-learning-Assisted MTL for a direct e2e many-To-many MTL scenario. The method can transfer model structures and weights between distributed devices without relying on powerful centralized servers. The effectiveness of the proposed approach is demonstrated by applying it to various scenarios. © 2014 IEEE.

关键词： Feature extraction

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Design and Development of a Peltier Cooling System for Photovoltaic Thermal Management Using Simulation Tools 4

Design and Development of a Peltier Cooling System for Photo...

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4th International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2025

作者： Oeishee, Moslema Hoque Muyeed, Munyem Ahammad Mehedi, Md. Rejoan Nidhi, Ramisa Sharar Brac University Electrical and Electronic Engineering Department Dhaka Bangladesh Brac University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798331530938

Mitigating the adverse effect of high temperature on photovoltaic (PV) module's efficiency in hot environment by using a thermoelectric cooling (TEC) method with PV through a detailed analysis, is the pivot objective of this paper. This research evaluates the Peltier module's effect on the power output and temperature of the PV modules in different ambient situations for the active cooling mode of the Peltier module. We utilized PVsyst and MATLAB (for initial circuit designs), as the simulation tools to analyze the performance ratio (PR) and thermal loss factor of the PV system for both with and without cooling conditions. Moreover, under an ambient temperature of 25.2°C, our developed TEC system enhances the PV module's performance ratio ranging from 83.42% to 85.8%, while The thermal loss factor is decreased ranging from -10.36% to -6.7%. These findings indicate that controlled cooling is capable of considerably improving PV performance, resulting in significant energy output rise. In addition to creating new paths to achieve more effective and sustainable energy production, this study demonstrates the way TEC systems might enhance solar energy systems, specifically in hot environments. © 2025 IEEE.

关键词： MATLAB

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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

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Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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Unsupervised IC Security with Machine Learning for Trojan Detection 66

Unsupervised IC Security with Machine Learning for Trojan De...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Ghimire, Ashutosh Amsaad, Fathi Hoque, Tamzidul Hopkinson, Kenneth Rahman, Md Tauhidur Wright State University Department of Computer Science and Engineering United States University of Kansas Department of Electrical Engineering and Computer Science United States Air Force Institute of Technology Department of Computer Science United States Florida International University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350302103

The detection of Hardware Trojans is crucial for ensuring trust in the semiconductor IC supply chain. However, existing detection methods that rely on side-channel analysis often require golden chips for verification. This paper presents a new approach to Hardware Trojan detection that utilizes unsupervised machine learning with side-channel analysis to eliminate the need for golden data. Trojans of varying sizes were implemented on an FPGA to evaluate the method to perform unsupervised clustering and detect anomalies. The proposed model achieved high accuracy, 93%, and improved the detection of small and short-triggered Trojans compared to competing approaches. The unsupervised learning techniques demonstrated a better false positive rate and similar accuracy to supervised approaches such as the KNN classifier, SVM, and Gaussian classifier which require golden data for training. This research contributes a new approach to Hardware Trojan detection that can improve the trustworthiness of semiconductor IC supply chains. © 2023 IEEE.

关键词： BIRCH Clustering FPGA IC Security side-channel Trojan unsupervised

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